{"id":"https://openalex.org/W4382141756","doi":"https://doi.org/10.48550/arxiv.2306.13210","title":"Directional diffusion models for graph representation learning","display_name":"Directional diffusion models for graph representation learning","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4382141756","doi":"https://doi.org/10.48550/arxiv.2306.13210"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2306.13210","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2306.13210","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109599028","display_name":"Run Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Run","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110057006","display_name":"Yuling Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yuling","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100403505","display_name":"Fan Zhou","orcid":"https://orcid.org/0000-0002-8038-8150"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Fan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100377834","display_name":"Qiang Sun","orcid":"https://orcid.org/0000-0003-3872-7267"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Qiang","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":67},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9813,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9813,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9674,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/representation","display_name":"Representation","score":0.50610775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.732601},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6116878},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5736425},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.50610775},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.48900545},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47476578},{"id":"https://openalex.org/C68710425","wikidata":"https://www.wikidata.org/wiki/Q5275442","display_name":"Diffusion process","level":3,"score":0.42864233},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35612887},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.0},{"id":"https://openalex.org/C3017618536","wikidata":"https://www.wikidata.org/wiki/Q304994","display_name":"Innovation diffusion","level":2,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2306.13210","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2306.13210","pdf_url":"http://arxiv.org/pdf/2306.13210","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2306.13210","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2306.13210","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4324271173","https://openalex.org/W4224009465","https://openalex.org/W2961085424","https://openalex.org/W2795079307","https://openalex.org/W2793058541","https://openalex.org/W2352227742","https://openalex.org/W2062195135","https://openalex.org/W2055929693","https://openalex.org/W1983629434","https://openalex.org/W1967645776"],"abstract_inverted_index":{"In":[0,38],"recent":[1],"years,":[2],"diffusion":[3,28,50,74,139,152,211],"models":[4,29,51,135,142,187,212,221],"have":[5],"achieved":[6],"remarkable":[7],"success":[8],"in":[9,30,76,121,149,194],"various":[10,223],"domains":[11],"of":[12,27,49,64,70,109,117,134,158,185,210,219],"artificial":[13],"intelligence,":[14],"such":[15],"as":[16],"image":[17],"synthesis,":[18],"super-resolution,":[19],"and":[20,66,113,146],"3D":[21],"molecule":[22],"generation.":[23],"However,":[24],"the":[25,47,61,71,91,96,107,115,122,150,156,183,207,216],"application":[26],"graph":[31,54,175,197],"learning":[32,77,177],"has":[33],"received":[34],"relatively":[35],"little":[36],"attention.":[37],"this":[39,43,127],"paper,":[40],"we":[41,129,162],"address":[42,126],"gap":[44],"by":[45,59],"investigating":[46],"use":[48],"for":[52,222],"unsupervised":[53],"representation":[55,176],"learning.":[56],"We":[57],"begin":[58],"identifying":[60],"anisotropic":[62,78,97],"structures":[63],"graphs":[65],"a":[67,131],"crucial":[68],"limitation":[69],"vanilla":[72],"forward":[73,151,208],"process":[75,81,209],"structures.":[79],"This":[80,103],"relies":[82],"on":[83,166,172],"continuously":[84],"adding":[85],"an":[86],"isotropic":[87],"Gaussian":[88],"noise":[89,100],"to":[90,99],"data,":[92],"which":[93],"may":[94],"convert":[95],"signals":[98],"too":[101],"quickly.":[102],"rapid":[104],"conversion":[105],"hampers":[106],"training":[108],"denoising":[110],"neural":[111],"networks":[112],"impedes":[114],"acquisition":[116],"semantically":[118],"meaningful":[119,196],"representations":[120],"reverse":[123],"process.":[124,153],"To":[125,154],"challenge,":[128],"propose":[130],"new":[132],"class":[133],"called":[136],"{\\it":[137],"directional":[138,147],"models}.":[140],"These":[141],"incorporate":[143],"data-dependent,":[144],"anisotropic,":[145],"noises":[148],"assess":[155],"efficacy":[157],"our":[159,186],"proposed":[160],"models,":[161],"conduct":[163],"extensive":[164],"experiments":[165],"12":[167],"publicly":[168],"available":[169],"datasets,":[170],"focusing":[171],"two":[173],"distinct":[174],"tasks.":[178,225],"The":[179],"experimental":[180],"results":[181],"demonstrate":[182],"superiority":[184],"over":[188],"state-of-the-art":[189],"baselines,":[190],"indicating":[191],"their":[192],"effectiveness":[193],"capturing":[195],"representations.":[198],"Our":[199],"studies":[200],"not":[201],"only":[202],"provide":[203],"valuable":[204],"insights":[205],"into":[206],"but":[213],"also":[214],"highlight":[215],"wide-ranging":[217],"potential":[218],"these":[220],"graph-related":[224]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4382141756","counts_by_year":[],"updated_date":"2025-01-21T06:40:01.895701","created_date":"2023-06-27"}